Overview

Dataset statistics

Number of variables33
Number of observations7043
Missing cells5185
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory264.0 B

Variable types

Categorical19
Numeric8
Boolean6

Alerts

Count has constant value "1" Constant
Country has constant value "United States" Constant
State has constant value "California" Constant
CustomerID has a high cardinality: 7043 distinct values High cardinality
City has a high cardinality: 1129 distinct values High cardinality
Lat Long has a high cardinality: 1652 distinct values High cardinality
Zip Code is highly correlated with Latitude and 1 other fieldsHigh correlation
Latitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Longitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Tenure Months is highly correlated with Total ChargesHigh correlation
Monthly Charges is highly correlated with Total ChargesHigh correlation
Total Charges is highly correlated with Tenure Months and 1 other fieldsHigh correlation
Churn Value is highly correlated with Churn ScoreHigh correlation
Churn Score is highly correlated with Churn ValueHigh correlation
Zip Code is highly correlated with Latitude and 1 other fieldsHigh correlation
Latitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Longitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Tenure Months is highly correlated with Total ChargesHigh correlation
Monthly Charges is highly correlated with Total ChargesHigh correlation
Total Charges is highly correlated with Tenure Months and 1 other fieldsHigh correlation
Churn Value is highly correlated with Churn ScoreHigh correlation
Churn Score is highly correlated with Churn ValueHigh correlation
Zip Code is highly correlated with LatitudeHigh correlation
Latitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Longitude is highly correlated with LatitudeHigh correlation
Tenure Months is highly correlated with Total ChargesHigh correlation
Total Charges is highly correlated with Tenure MonthsHigh correlation
Churn Value is highly correlated with Churn ScoreHigh correlation
Churn Score is highly correlated with Churn ValueHigh correlation
Streaming TV is highly correlated with Internet Service and 8 other fieldsHigh correlation
Partner is highly correlated with Country and 2 other fieldsHigh correlation
Dependents is highly correlated with Country and 2 other fieldsHigh correlation
Multiple Lines is highly correlated with Phone Service and 3 other fieldsHigh correlation
Paperless Billing is highly correlated with Country and 2 other fieldsHigh correlation
Internet Service is highly correlated with Streaming TV and 8 other fieldsHigh correlation
Device Protection is highly correlated with Streaming TV and 8 other fieldsHigh correlation
Payment Method is highly correlated with Country and 2 other fieldsHigh correlation
Streaming Movies is highly correlated with Streaming TV and 8 other fieldsHigh correlation
Senior Citizen is highly correlated with Country and 2 other fieldsHigh correlation
Online Backup is highly correlated with Streaming TV and 8 other fieldsHigh correlation
Tech Support is highly correlated with Streaming TV and 8 other fieldsHigh correlation
Churn Value is highly correlated with Country and 4 other fieldsHigh correlation
Phone Service is highly correlated with Multiple Lines and 3 other fieldsHigh correlation
Gender is highly correlated with Country and 2 other fieldsHigh correlation
Country is highly correlated with Streaming TV and 20 other fieldsHigh correlation
Contract is highly correlated with Country and 2 other fieldsHigh correlation
Churn Label is highly correlated with Churn Value and 4 other fieldsHigh correlation
Online Security is highly correlated with Streaming TV and 8 other fieldsHigh correlation
State is highly correlated with Streaming TV and 20 other fieldsHigh correlation
Count is highly correlated with Streaming TV and 20 other fieldsHigh correlation
Churn Reason is highly correlated with Churn Value and 4 other fieldsHigh correlation
Zip Code is highly correlated with Latitude and 1 other fieldsHigh correlation
Latitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Longitude is highly correlated with Zip Code and 1 other fieldsHigh correlation
Partner is highly correlated with DependentsHigh correlation
Dependents is highly correlated with PartnerHigh correlation
Tenure Months is highly correlated with Contract and 1 other fieldsHigh correlation
Phone Service is highly correlated with Multiple Lines and 1 other fieldsHigh correlation
Multiple Lines is highly correlated with Phone Service and 8 other fieldsHigh correlation
Internet Service is highly correlated with Multiple Lines and 9 other fieldsHigh correlation
Online Security is highly correlated with Multiple Lines and 9 other fieldsHigh correlation
Online Backup is highly correlated with Multiple Lines and 9 other fieldsHigh correlation
Device Protection is highly correlated with Multiple Lines and 9 other fieldsHigh correlation
Tech Support is highly correlated with Multiple Lines and 9 other fieldsHigh correlation
Streaming TV is highly correlated with Multiple Lines and 9 other fieldsHigh correlation
Streaming Movies is highly correlated with Multiple Lines and 9 other fieldsHigh correlation
Contract is highly correlated with Tenure Months and 8 other fieldsHigh correlation
Monthly Charges is highly correlated with Phone Service and 9 other fieldsHigh correlation
Total Charges is highly correlated with Tenure Months and 9 other fieldsHigh correlation
Churn Label is highly correlated with Churn Value and 1 other fieldsHigh correlation
Churn Value is highly correlated with Churn Label and 1 other fieldsHigh correlation
Churn Score is highly correlated with Churn Label and 1 other fieldsHigh correlation
Churn Reason has 5174 (73.5%) missing values Missing
CustomerID is uniformly distributed Uniform
Lat Long is uniformly distributed Uniform
CustomerID has unique values Unique

Reproduction

Analysis started2022-09-11 10:53:30.761609
Analysis finished2022-09-11 10:53:38.276133
Duration7.51 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

CustomerID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
3668-QPYBK
 
1
9169-BSVIN
 
1
0206-OYVOC
 
1
6418-HNFED
 
1
8805-JNRAZ
 
1
Other values (7038)
7038 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7043 ?
Unique (%)100.0%

Sample

1st row3668-QPYBK
2nd row9237-HQITU
3rd row9305-CDSKC
4th row7892-POOKP
5th row0280-XJGEX

Common Values

ValueCountFrequency (%)
3668-QPYBK1
 
< 0.1%
9169-BSVIN1
 
< 0.1%
0206-OYVOC1
 
< 0.1%
6418-HNFED1
 
< 0.1%
8805-JNRAZ1
 
< 0.1%
8439-LTUGF1
 
< 0.1%
1767-TGTKO1
 
< 0.1%
1194-HVAIF1
 
< 0.1%
1080-BWSYE1
 
< 0.1%
8387-UGUSU1
 
< 0.1%
Other values (7033)7033
99.9%

Length

2022-09-11T17:53:38.303819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3668-qpybk1
 
< 0.1%
8168-uqwwf1
 
< 0.1%
7892-pookp1
 
< 0.1%
0280-xjgex1
 
< 0.1%
4190-mfluw1
 
< 0.1%
8779-qrdmv1
 
< 0.1%
1066-jksgk1
 
< 0.1%
6467-chfzw1
 
< 0.1%
8665-utdhz1
 
< 0.1%
8773-hhuoz1
 
< 0.1%
Other values (7033)7033
99.9%

Most occurring characters

ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter35215
50.0%
Decimal Number28172
40.0%
Dash Punctuation7043
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O1442
 
4.1%
H1396
 
4.0%
B1393
 
4.0%
S1386
 
3.9%
V1382
 
3.9%
T1374
 
3.9%
Z1368
 
3.9%
C1368
 
3.9%
L1363
 
3.9%
F1363
 
3.9%
Other values (16)21380
60.7%
Decimal Number
ValueCountFrequency (%)
22901
10.3%
92881
10.2%
62870
10.2%
72836
10.1%
02831
10.0%
82812
10.0%
52810
10.0%
32791
9.9%
12726
9.7%
42714
9.6%
Dash Punctuation
ValueCountFrequency (%)
-7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35215
50.0%
Latin35215
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O1442
 
4.1%
H1396
 
4.0%
B1393
 
4.0%
S1386
 
3.9%
V1382
 
3.9%
T1374
 
3.9%
Z1368
 
3.9%
C1368
 
3.9%
L1363
 
3.9%
F1363
 
3.9%
Other values (16)21380
60.7%
Common
ValueCountFrequency (%)
-7043
20.0%
22901
8.2%
92881
8.2%
62870
8.1%
72836
8.1%
02831
8.0%
82812
 
8.0%
52810
 
8.0%
32791
 
7.9%
12726
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII70430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-7043
 
10.0%
22901
 
4.1%
92881
 
4.1%
62870
 
4.1%
72836
 
4.0%
02831
 
4.0%
82812
 
4.0%
52810
 
4.0%
32791
 
4.0%
12726
 
3.9%
Other values (27)37929
53.9%

Count
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
1
7043 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17043
100.0%

Length

2022-09-11T17:53:38.340152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:38.377364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
17043
100.0%

Most occurring characters

ValueCountFrequency (%)
17043
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7043
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17043
100.0%

Country
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
United States
7043 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters91559
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States7043
100.0%

Length

2022-09-11T17:53:38.406433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:38.440218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
united7043
50.0%
states7043
50.0%

Most occurring characters

ValueCountFrequency (%)
t21129
23.1%
e14086
15.4%
U7043
 
7.7%
n7043
 
7.7%
i7043
 
7.7%
d7043
 
7.7%
7043
 
7.7%
S7043
 
7.7%
a7043
 
7.7%
s7043
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter70430
76.9%
Uppercase Letter14086
 
15.4%
Space Separator7043
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t21129
30.0%
e14086
20.0%
n7043
 
10.0%
i7043
 
10.0%
d7043
 
10.0%
a7043
 
10.0%
s7043
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
U7043
50.0%
S7043
50.0%
Space Separator
ValueCountFrequency (%)
7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84516
92.3%
Common7043
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t21129
25.0%
e14086
16.7%
U7043
 
8.3%
n7043
 
8.3%
i7043
 
8.3%
d7043
 
8.3%
S7043
 
8.3%
a7043
 
8.3%
s7043
 
8.3%
Common
ValueCountFrequency (%)
7043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII91559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t21129
23.1%
e14086
15.4%
U7043
 
7.7%
n7043
 
7.7%
i7043
 
7.7%
d7043
 
7.7%
7043
 
7.7%
S7043
 
7.7%
a7043
 
7.7%
s7043
 
7.7%

State
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
California
7043 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters70430
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCalifornia
2nd rowCalifornia
3rd rowCalifornia
4th rowCalifornia
5th rowCalifornia

Common Values

ValueCountFrequency (%)
California7043
100.0%

Length

2022-09-11T17:53:38.469922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:38.503481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
california7043
100.0%

Most occurring characters

ValueCountFrequency (%)
a14086
20.0%
i14086
20.0%
C7043
10.0%
l7043
10.0%
f7043
10.0%
o7043
10.0%
r7043
10.0%
n7043
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63387
90.0%
Uppercase Letter7043
 
10.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a14086
22.2%
i14086
22.2%
l7043
11.1%
f7043
11.1%
o7043
11.1%
r7043
11.1%
n7043
11.1%
Uppercase Letter
ValueCountFrequency (%)
C7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin70430
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a14086
20.0%
i14086
20.0%
C7043
10.0%
l7043
10.0%
f7043
10.0%
o7043
10.0%
r7043
10.0%
n7043
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII70430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a14086
20.0%
i14086
20.0%
C7043
10.0%
l7043
10.0%
f7043
10.0%
o7043
10.0%
r7043
10.0%
n7043
10.0%

City
Categorical

HIGH CARDINALITY

Distinct1129
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Los Angeles
 
305
San Diego
 
150
San Jose
 
112
Sacramento
 
108
San Francisco
 
104
Other values (1124)
6264 

Length

Max length22
Median length19
Mean length9.223200341
Min length3

Characters and Unicode

Total characters64959
Distinct characters52
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLos Angeles
2nd rowLos Angeles
3rd rowLos Angeles
4th rowLos Angeles
5th rowLos Angeles

Common Values

ValueCountFrequency (%)
Los Angeles305
 
4.3%
San Diego150
 
2.1%
San Jose112
 
1.6%
Sacramento108
 
1.5%
San Francisco104
 
1.5%
Fresno64
 
0.9%
Long Beach60
 
0.9%
Oakland52
 
0.7%
Stockton44
 
0.6%
Bakersfield40
 
0.6%
Other values (1119)6004
85.2%

Length

2022-09-11T17:53:38.540499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san584
 
5.7%
los350
 
3.4%
angeles305
 
3.0%
valley183
 
1.8%
santa182
 
1.8%
beach172
 
1.7%
city165
 
1.6%
diego150
 
1.5%
sacramento116
 
1.1%
jose112
 
1.1%
Other values (1131)8000
77.5%

Most occurring characters

ValueCountFrequency (%)
a6951
 
10.7%
e6152
 
9.5%
n5117
 
7.9%
o4943
 
7.6%
l4007
 
6.2%
r3649
 
5.6%
i3371
 
5.2%
3276
 
5.0%
s2924
 
4.5%
t2708
 
4.2%
Other values (42)21861
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51364
79.1%
Uppercase Letter10319
 
15.9%
Space Separator3276
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6951
13.5%
e6152
12.0%
n5117
10.0%
o4943
9.6%
l4007
7.8%
r3649
 
7.1%
i3371
 
6.6%
s2924
 
5.7%
t2708
 
5.3%
d1649
 
3.2%
Other values (16)9893
19.3%
Uppercase Letter
ValueCountFrequency (%)
S1465
14.2%
C1010
 
9.8%
L901
 
8.7%
B752
 
7.3%
A673
 
6.5%
M619
 
6.0%
P613
 
5.9%
R456
 
4.4%
F442
 
4.3%
V420
 
4.1%
Other values (15)2968
28.8%
Space Separator
ValueCountFrequency (%)
3276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61683
95.0%
Common3276
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6951
 
11.3%
e6152
 
10.0%
n5117
 
8.3%
o4943
 
8.0%
l4007
 
6.5%
r3649
 
5.9%
i3371
 
5.5%
s2924
 
4.7%
t2708
 
4.4%
d1649
 
2.7%
Other values (41)20212
32.8%
Common
ValueCountFrequency (%)
3276
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII64959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a6951
 
10.7%
e6152
 
9.5%
n5117
 
7.9%
o4943
 
7.6%
l4007
 
6.2%
r3649
 
5.6%
i3371
 
5.2%
3276
 
5.0%
s2924
 
4.5%
t2708
 
4.2%
Other values (42)21861
33.7%

Zip Code
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93521.96465
Minimum90001
Maximum96161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-09-11T17:53:38.592978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum90001
5-th percentile90232
Q192102
median93552
Q395351
95-th percentile96031
Maximum96161
Range6160
Interquartile range (IQR)3249

Descriptive statistics

Standard deviation1865.794555
Coefficient of variation (CV)0.01995033533
Kurtosis-1.154042612
Mean93521.96465
Median Absolute Deviation (MAD)1641
Skewness-0.251463488
Sum658675197
Variance3481189.323
MonotonicityNot monotonic
2022-09-11T17:53:38.642911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
900035
 
0.1%
914365
 
0.1%
919165
 
0.1%
919135
 
0.1%
919115
 
0.1%
917865
 
0.1%
917845
 
0.1%
917805
 
0.1%
917655
 
0.1%
917595
 
0.1%
Other values (1642)6993
99.3%
ValueCountFrequency (%)
900015
0.1%
900025
0.1%
900035
0.1%
900045
0.1%
900055
0.1%
900065
0.1%
900075
0.1%
900085
0.1%
900105
0.1%
900115
0.1%
ValueCountFrequency (%)
961614
0.1%
961504
0.1%
961484
0.1%
961464
0.1%
961454
0.1%
961434
0.1%
961424
0.1%
961414
0.1%
961404
0.1%
961374
0.1%

Lat Long
Categorical

HIGH CARDINALITY
UNIFORM

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
33.964131, -118.272783
 
5
34.152875, -118.486056
 
5
32.912664, -116.635387
 
5
32.64164, -116.985026
 
5
32.607964, -117.059459
 
5
Other values (1647)
7018 

Length

Max length22
Median length22
Mean length21.77708363
Min length18

Characters and Unicode

Total characters153376
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33.964131, -118.272783
2nd row34.059281, -118.30742
3rd row34.048013, -118.293953
4th row34.062125, -118.315709
5th row34.039224, -118.266293

Common Values

ValueCountFrequency (%)
33.964131, -118.2727835
 
0.1%
34.152875, -118.4860565
 
0.1%
32.912664, -116.6353875
 
0.1%
32.64164, -116.9850265
 
0.1%
32.607964, -117.0594595
 
0.1%
34.105493, -117.6609345
 
0.1%
34.141146, -117.6555835
 
0.1%
34.101608, -118.0558485
 
0.1%
33.992416, -117.8078745
 
0.1%
34.231318, -117.6620325
 
0.1%
Other values (1642)6993
99.3%

Length

2022-09-11T17:53:38.818265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
121.9948138
 
0.1%
33.9641315
 
< 0.1%
118.0626115
 
< 0.1%
33.9408845
 
< 0.1%
118.1286285
 
< 0.1%
33.7716125
 
< 0.1%
118.1438665
 
< 0.1%
33.8336995
 
< 0.1%
118.3143875
 
< 0.1%
33.8078825
 
< 0.1%
Other values (3293)14033
99.6%

Most occurring characters

ValueCountFrequency (%)
120397
13.3%
316446
10.7%
.14086
9.2%
213706
8.9%
811002
 
7.2%
710782
 
7.0%
410512
 
6.9%
99545
 
6.2%
68950
 
5.8%
58675
 
5.7%
Other values (4)29275
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number118161
77.0%
Other Punctuation21129
 
13.8%
Space Separator7043
 
4.6%
Dash Punctuation7043
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
120397
17.3%
316446
13.9%
213706
11.6%
811002
9.3%
710782
9.1%
410512
8.9%
99545
8.1%
68950
7.6%
58675
7.3%
08146
 
6.9%
Other Punctuation
ValueCountFrequency (%)
.14086
66.7%
,7043
33.3%
Space Separator
ValueCountFrequency (%)
7043
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common153376
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
120397
13.3%
316446
10.7%
.14086
9.2%
213706
8.9%
811002
 
7.2%
710782
 
7.0%
410512
 
6.9%
99545
 
6.2%
68950
 
5.8%
58675
 
5.7%
Other values (4)29275
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII153376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120397
13.3%
316446
10.7%
.14086
9.2%
213706
8.9%
811002
 
7.2%
710782
 
7.0%
410512
 
6.9%
99545
 
6.2%
68950
 
5.8%
58675
 
5.7%
Other values (4)29275
19.1%

Latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1652
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.28244138
Minimum32.555828
Maximum41.962127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-09-11T17:53:38.866219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum32.555828
5-th percentile32.980678
Q134.030915
median36.391777
Q338.224869
95-th percentile40.557314
Maximum41.962127
Range9.406299
Interquartile range (IQR)4.193954

Descriptive statistics

Standard deviation2.45572259
Coefficient of variation (CV)0.06768349913
Kurtosis-1.135607142
Mean36.28244138
Median Absolute Deviation (MAD)2.263493
Skewness0.3038672929
Sum255537.2346
Variance6.030573437
MonotonicityNot monotonic
2022-09-11T17:53:38.916267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.9641315
 
0.1%
34.1528755
 
0.1%
32.9126645
 
0.1%
32.641645
 
0.1%
32.6079645
 
0.1%
34.1054935
 
0.1%
34.1411465
 
0.1%
34.1016085
 
0.1%
33.9924165
 
0.1%
34.2313185
 
0.1%
Other values (1642)6993
99.3%
ValueCountFrequency (%)
32.5558285
0.1%
32.5781035
0.1%
32.5791345
0.1%
32.5875575
0.1%
32.6050125
0.1%
32.6079645
0.1%
32.6194655
0.1%
32.6229995
0.1%
32.6367925
0.1%
32.641645
0.1%
ValueCountFrequency (%)
41.9621274
0.1%
41.9506834
0.1%
41.9492164
0.1%
41.9322074
0.1%
41.9241744
0.1%
41.8679084
0.1%
41.8319014
0.1%
41.8165954
0.1%
41.8135214
0.1%
41.7697094
0.1%

Longitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1651
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.7988801
Minimum-124.301372
Maximum-114.192901
Zeros0
Zeros (%)0.0%
Negative7043
Negative (%)100.0%
Memory size55.1 KiB
2022-09-11T17:53:38.965396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-124.301372
5-th percentile-122.998726
Q1-121.815412
median-119.730885
Q3-118.043237
95-th percentile-116.76058
Maximum-114.192901
Range10.108471
Interquartile range (IQR)3.772175

Descriptive statistics

Standard deviation2.157889095
Coefficient of variation (CV)-0.01801259823
Kurtosis-1.136049757
Mean-119.7988801
Median Absolute Deviation (MAD)1.824786
Skewness-0.04079238284
Sum-843743.5124
Variance4.656485347
MonotonicityNot monotonic
2022-09-11T17:53:39.012943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-121.9948138
 
0.1%
-118.2727835
 
0.1%
-117.6620325
 
0.1%
-116.6353875
 
0.1%
-116.9850265
 
0.1%
-117.0594595
 
0.1%
-117.6609345
 
0.1%
-117.6555835
 
0.1%
-118.0558485
 
0.1%
-117.8078745
 
0.1%
Other values (1641)6990
99.2%
ValueCountFrequency (%)
-124.3013724
0.1%
-124.2400514
0.1%
-124.2173784
0.1%
-124.2109024
0.1%
-124.1899774
0.1%
-124.1632344
0.1%
-124.154284
0.1%
-124.1215044
0.1%
-124.1088974
0.1%
-124.0987394
0.1%
ValueCountFrequency (%)
-114.1929015
0.1%
-114.365145
0.1%
-114.7022564
0.1%
-114.716125
0.1%
-114.7179645
0.1%
-114.7583345
0.1%
-114.8507845
0.1%
-115.1528655
0.1%
-115.1918575
0.1%
-115.2570095
0.1%

Gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Male
3555 
Female
3488 

Length

Max length6
Median length4
Mean length4.990487008
Min length4

Characters and Unicode

Total characters35148
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male3555
50.5%
Female3488
49.5%

Length

2022-09-11T17:53:39.054533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.091770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
male3555
50.5%
female3488
49.5%

Most occurring characters

ValueCountFrequency (%)
e10531
30.0%
a7043
20.0%
l7043
20.0%
M3555
 
10.1%
F3488
 
9.9%
m3488
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter28105
80.0%
Uppercase Letter7043
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10531
37.5%
a7043
25.1%
l7043
25.1%
m3488
 
12.4%
Uppercase Letter
ValueCountFrequency (%)
M3555
50.5%
F3488
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin35148
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10531
30.0%
a7043
20.0%
l7043
20.0%
M3555
 
10.1%
F3488
 
9.9%
m3488
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII35148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10531
30.0%
a7043
20.0%
l7043
20.0%
M3555
 
10.1%
F3488
 
9.9%
m3488
 
9.9%

Senior Citizen
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5901 
True
1142 
ValueCountFrequency (%)
False5901
83.8%
True1142
 
16.2%
2022-09-11T17:53:39.124859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Partner
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
3641 
True
3402 
ValueCountFrequency (%)
False3641
51.7%
True3402
48.3%
2022-09-11T17:53:39.157914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Dependents
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5416 
True
1627 
ValueCountFrequency (%)
False5416
76.9%
True1627
 
23.1%
2022-09-11T17:53:39.190493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Tenure Months
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct73
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.37114866
Minimum0
Maximum72
Zeros11
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-09-11T17:53:39.226780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median29
Q355
95-th percentile72
Maximum72
Range72
Interquartile range (IQR)46

Descriptive statistics

Standard deviation24.55948102
Coefficient of variation (CV)0.7586842618
Kurtosis-1.387371636
Mean32.37114866
Median Absolute Deviation (MAD)22
Skewness0.2395397496
Sum227990
Variance603.1681081
MonotonicityNot monotonic
2022-09-11T17:53:39.272244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1613
 
8.7%
72362
 
5.1%
2238
 
3.4%
3200
 
2.8%
4176
 
2.5%
71170
 
2.4%
5133
 
1.9%
7131
 
1.9%
8123
 
1.7%
70119
 
1.7%
Other values (63)4778
67.8%
ValueCountFrequency (%)
011
 
0.2%
1613
8.7%
2238
 
3.4%
3200
 
2.8%
4176
 
2.5%
5133
 
1.9%
6110
 
1.6%
7131
 
1.9%
8123
 
1.7%
9119
 
1.7%
ValueCountFrequency (%)
72362
5.1%
71170
2.4%
70119
 
1.7%
6995
 
1.3%
68100
 
1.4%
6798
 
1.4%
6689
 
1.3%
6576
 
1.1%
6480
 
1.1%
6372
 
1.0%

Phone Service
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
6361 
False
682 
ValueCountFrequency (%)
True6361
90.3%
False682
 
9.7%
2022-09-11T17:53:39.312785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Multiple Lines
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3390 
Yes
2971 
No phone service
682 

Length

Max length16
Median length3
Mean length3.777509584
Min length2

Characters and Unicode

Total characters26605
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No3390
48.1%
Yes2971
42.2%
No phone service682
 
9.7%

Length

2022-09-11T17:53:39.345700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.383771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no4072
48.4%
yes2971
35.3%
phone682
 
8.1%
service682
 
8.1%

Most occurring characters

ValueCountFrequency (%)
e5017
18.9%
o4754
17.9%
N4072
15.3%
s3653
13.7%
Y2971
11.2%
1364
 
5.1%
p682
 
2.6%
h682
 
2.6%
n682
 
2.6%
r682
 
2.6%
Other values (3)2046
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18198
68.4%
Uppercase Letter7043
 
26.5%
Space Separator1364
 
5.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5017
27.6%
o4754
26.1%
s3653
20.1%
p682
 
3.7%
h682
 
3.7%
n682
 
3.7%
r682
 
3.7%
v682
 
3.7%
i682
 
3.7%
c682
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
N4072
57.8%
Y2971
42.2%
Space Separator
ValueCountFrequency (%)
1364
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25241
94.9%
Common1364
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5017
19.9%
o4754
18.8%
N4072
16.1%
s3653
14.5%
Y2971
11.8%
p682
 
2.7%
h682
 
2.7%
n682
 
2.7%
r682
 
2.7%
v682
 
2.7%
Other values (2)1364
 
5.4%
Common
ValueCountFrequency (%)
1364
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII26605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5017
18.9%
o4754
17.9%
N4072
15.3%
s3653
13.7%
Y2971
11.2%
1364
 
5.1%
p682
 
2.6%
h682
 
2.6%
n682
 
2.6%
r682
 
2.6%
Other values (3)2046
7.7%

Internet Service
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Fiber optic
3096 
DSL
2421 
No
1526 

Length

Max length11
Median length3
Mean length6.300014198
Min length2

Characters and Unicode

Total characters44371
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDSL
2nd rowFiber optic
3rd rowFiber optic
4th rowFiber optic
5th rowFiber optic

Common Values

ValueCountFrequency (%)
Fiber optic3096
44.0%
DSL2421
34.4%
No1526
21.7%

Length

2022-09-11T17:53:39.417054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.454136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
fiber3096
30.5%
optic3096
30.5%
dsl2421
23.9%
no1526
15.1%

Most occurring characters

ValueCountFrequency (%)
i6192
14.0%
o4622
10.4%
F3096
 
7.0%
b3096
 
7.0%
e3096
 
7.0%
r3096
 
7.0%
3096
 
7.0%
p3096
 
7.0%
t3096
 
7.0%
c3096
 
7.0%
Other values (4)8789
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter29390
66.2%
Uppercase Letter11885
26.8%
Space Separator3096
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i6192
21.1%
o4622
15.7%
b3096
10.5%
e3096
10.5%
r3096
10.5%
p3096
10.5%
t3096
10.5%
c3096
10.5%
Uppercase Letter
ValueCountFrequency (%)
F3096
26.0%
D2421
20.4%
S2421
20.4%
L2421
20.4%
N1526
12.8%
Space Separator
ValueCountFrequency (%)
3096
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin41275
93.0%
Common3096
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i6192
15.0%
o4622
11.2%
F3096
7.5%
b3096
7.5%
e3096
7.5%
r3096
7.5%
p3096
7.5%
t3096
7.5%
c3096
7.5%
D2421
 
5.9%
Other values (3)6368
15.4%
Common
ValueCountFrequency (%)
3096
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII44371
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i6192
14.0%
o4622
10.4%
F3096
 
7.0%
b3096
 
7.0%
e3096
 
7.0%
r3096
 
7.0%
3096
 
7.0%
p3096
 
7.0%
t3096
 
7.0%
c3096
 
7.0%
Other values (4)8789
19.8%

Online Security
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3498 
Yes
2019 
No internet service
1526 

Length

Max length19
Median length3
Mean length5.970041176
Min length2

Characters and Unicode

Total characters42047
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No3498
49.7%
Yes2019
28.7%
No internet service1526
21.7%

Length

2022-09-11T17:53:39.487595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.524708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no5024
49.8%
yes2019
20.0%
internet1526
 
15.1%
service1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e8123
19.3%
N5024
11.9%
o5024
11.9%
s3545
8.4%
3052
 
7.3%
i3052
 
7.3%
n3052
 
7.3%
t3052
 
7.3%
r3052
 
7.3%
Y2019
 
4.8%
Other values (2)3052
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31952
76.0%
Uppercase Letter7043
 
16.8%
Space Separator3052
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8123
25.4%
o5024
15.7%
s3545
11.1%
i3052
 
9.6%
n3052
 
9.6%
t3052
 
9.6%
r3052
 
9.6%
v1526
 
4.8%
c1526
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
N5024
71.3%
Y2019
28.7%
Space Separator
ValueCountFrequency (%)
3052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin38995
92.7%
Common3052
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8123
20.8%
N5024
12.9%
o5024
12.9%
s3545
9.1%
i3052
 
7.8%
n3052
 
7.8%
t3052
 
7.8%
r3052
 
7.8%
Y2019
 
5.2%
v1526
 
3.9%
Common
ValueCountFrequency (%)
3052
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8123
19.3%
N5024
11.9%
o5024
11.9%
s3545
8.4%
3052
 
7.3%
i3052
 
7.3%
n3052
 
7.3%
t3052
 
7.3%
r3052
 
7.3%
Y2019
 
4.8%
Other values (2)3052
 
7.3%

Online Backup
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3088 
Yes
2429 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.028255005
Min length2

Characters and Unicode

Total characters42457
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowNo
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
No3088
43.8%
Yes2429
34.5%
No internet service1526
21.7%

Length

2022-09-11T17:53:39.558049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.595278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no4614
45.7%
yes2429
24.1%
internet1526
 
15.1%
service1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e8533
20.1%
N4614
10.9%
o4614
10.9%
s3955
9.3%
3052
 
7.2%
i3052
 
7.2%
n3052
 
7.2%
t3052
 
7.2%
r3052
 
7.2%
Y2429
 
5.7%
Other values (2)3052
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32362
76.2%
Uppercase Letter7043
 
16.6%
Space Separator3052
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8533
26.4%
o4614
14.3%
s3955
12.2%
i3052
 
9.4%
n3052
 
9.4%
t3052
 
9.4%
r3052
 
9.4%
v1526
 
4.7%
c1526
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
N4614
65.5%
Y2429
34.5%
Space Separator
ValueCountFrequency (%)
3052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39405
92.8%
Common3052
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8533
21.7%
N4614
11.7%
o4614
11.7%
s3955
10.0%
i3052
 
7.7%
n3052
 
7.7%
t3052
 
7.7%
r3052
 
7.7%
Y2429
 
6.2%
v1526
 
3.9%
Common
ValueCountFrequency (%)
3052
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42457
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8533
20.1%
N4614
10.9%
o4614
10.9%
s3955
9.3%
3052
 
7.2%
i3052
 
7.2%
n3052
 
7.2%
t3052
 
7.2%
r3052
 
7.2%
Y2429
 
5.7%
Other values (2)3052
 
7.2%

Device Protection
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3095 
Yes
2422 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.02726111
Min length2

Characters and Unicode

Total characters42450
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No3095
43.9%
Yes2422
34.4%
No internet service1526
21.7%

Length

2022-09-11T17:53:39.628747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.666182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no4621
45.8%
yes2422
24.0%
internet1526
 
15.1%
service1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e8526
20.1%
N4621
10.9%
o4621
10.9%
s3948
9.3%
3052
 
7.2%
i3052
 
7.2%
n3052
 
7.2%
t3052
 
7.2%
r3052
 
7.2%
Y2422
 
5.7%
Other values (2)3052
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32355
76.2%
Uppercase Letter7043
 
16.6%
Space Separator3052
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8526
26.4%
o4621
14.3%
s3948
12.2%
i3052
 
9.4%
n3052
 
9.4%
t3052
 
9.4%
r3052
 
9.4%
v1526
 
4.7%
c1526
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
N4621
65.6%
Y2422
34.4%
Space Separator
ValueCountFrequency (%)
3052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39398
92.8%
Common3052
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8526
21.6%
N4621
11.7%
o4621
11.7%
s3948
10.0%
i3052
 
7.7%
n3052
 
7.7%
t3052
 
7.7%
r3052
 
7.7%
Y2422
 
6.1%
v1526
 
3.9%
Common
ValueCountFrequency (%)
3052
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8526
20.1%
N4621
10.9%
o4621
10.9%
s3948
9.3%
3052
 
7.2%
i3052
 
7.2%
n3052
 
7.2%
t3052
 
7.2%
r3052
 
7.2%
Y2422
 
5.7%
Other values (2)3052
 
7.2%

Tech Support
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
3473 
Yes
2044 
No internet service
1526 

Length

Max length19
Median length3
Mean length5.973590799
Min length2

Characters and Unicode

Total characters42072
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No3473
49.3%
Yes2044
29.0%
No internet service1526
21.7%

Length

2022-09-11T17:53:39.699594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.736960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no4999
49.5%
yes2044
20.2%
internet1526
 
15.1%
service1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e8148
19.4%
N4999
11.9%
o4999
11.9%
s3570
8.5%
3052
 
7.3%
i3052
 
7.3%
n3052
 
7.3%
t3052
 
7.3%
r3052
 
7.3%
Y2044
 
4.9%
Other values (2)3052
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter31977
76.0%
Uppercase Letter7043
 
16.7%
Space Separator3052
 
7.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8148
25.5%
o4999
15.6%
s3570
11.2%
i3052
 
9.5%
n3052
 
9.5%
t3052
 
9.5%
r3052
 
9.5%
v1526
 
4.8%
c1526
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
N4999
71.0%
Y2044
29.0%
Space Separator
ValueCountFrequency (%)
3052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39020
92.7%
Common3052
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8148
20.9%
N4999
12.8%
o4999
12.8%
s3570
9.1%
i3052
 
7.8%
n3052
 
7.8%
t3052
 
7.8%
r3052
 
7.8%
Y2044
 
5.2%
v1526
 
3.9%
Common
ValueCountFrequency (%)
3052
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42072
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8148
19.4%
N4999
11.9%
o4999
11.9%
s3570
8.5%
3052
 
7.3%
i3052
 
7.3%
n3052
 
7.3%
t3052
 
7.3%
r3052
 
7.3%
Y2044
 
4.9%
Other values (2)3052
 
7.3%

Streaming TV
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
2810 
Yes
2707 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.067726821
Min length2

Characters and Unicode

Total characters42735
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No2810
39.9%
Yes2707
38.4%
No internet service1526
21.7%

Length

2022-09-11T17:53:39.771235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.809660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no4336
43.0%
yes2707
26.8%
internet1526
 
15.1%
service1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e8811
20.6%
N4336
10.1%
o4336
10.1%
s4233
9.9%
3052
 
7.1%
i3052
 
7.1%
n3052
 
7.1%
t3052
 
7.1%
r3052
 
7.1%
Y2707
 
6.3%
Other values (2)3052
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32640
76.4%
Uppercase Letter7043
 
16.5%
Space Separator3052
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8811
27.0%
o4336
13.3%
s4233
13.0%
i3052
 
9.4%
n3052
 
9.4%
t3052
 
9.4%
r3052
 
9.4%
v1526
 
4.7%
c1526
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
N4336
61.6%
Y2707
38.4%
Space Separator
ValueCountFrequency (%)
3052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39683
92.9%
Common3052
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8811
22.2%
N4336
10.9%
o4336
10.9%
s4233
10.7%
i3052
 
7.7%
n3052
 
7.7%
t3052
 
7.7%
r3052
 
7.7%
Y2707
 
6.8%
v1526
 
3.8%
Common
ValueCountFrequency (%)
3052
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8811
20.6%
N4336
10.1%
o4336
10.1%
s4233
9.9%
3052
 
7.1%
i3052
 
7.1%
n3052
 
7.1%
t3052
 
7.1%
r3052
 
7.1%
Y2707
 
6.3%
Other values (2)3052
 
7.1%

Streaming Movies
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
No
2785 
Yes
2732 
No internet service
1526 

Length

Max length19
Median length3
Mean length6.071276445
Min length2

Characters and Unicode

Total characters42760
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
No2785
39.5%
Yes2732
38.8%
No internet service1526
21.7%

Length

2022-09-11T17:53:39.843573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.881884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
no4311
42.7%
yes2732
27.1%
internet1526
 
15.1%
service1526
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e8836
20.7%
N4311
10.1%
o4311
10.1%
s4258
10.0%
3052
 
7.1%
i3052
 
7.1%
n3052
 
7.1%
t3052
 
7.1%
r3052
 
7.1%
Y2732
 
6.4%
Other values (2)3052
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter32665
76.4%
Uppercase Letter7043
 
16.5%
Space Separator3052
 
7.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8836
27.1%
o4311
13.2%
s4258
13.0%
i3052
 
9.3%
n3052
 
9.3%
t3052
 
9.3%
r3052
 
9.3%
v1526
 
4.7%
c1526
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
N4311
61.2%
Y2732
38.8%
Space Separator
ValueCountFrequency (%)
3052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin39708
92.9%
Common3052
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8836
22.3%
N4311
10.9%
o4311
10.9%
s4258
10.7%
i3052
 
7.7%
n3052
 
7.7%
t3052
 
7.7%
r3052
 
7.7%
Y2732
 
6.9%
v1526
 
3.8%
Common
ValueCountFrequency (%)
3052
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII42760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e8836
20.7%
N4311
10.1%
o4311
10.1%
s4258
10.0%
3052
 
7.1%
i3052
 
7.1%
n3052
 
7.1%
t3052
 
7.1%
r3052
 
7.1%
Y2732
 
6.4%
Other values (2)3052
 
7.1%

Contract
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Month-to-month
3875 
Two year
1695 
One year
1473 

Length

Max length14
Median length14
Mean length11.30115008
Min length8

Characters and Unicode

Total characters79594
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMonth-to-month
2nd rowMonth-to-month
3rd rowMonth-to-month
4th rowMonth-to-month
5th rowMonth-to-month

Common Values

ValueCountFrequency (%)
Month-to-month3875
55.0%
Two year1695
24.1%
One year1473
 
20.9%

Length

2022-09-11T17:53:39.917252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:39.956088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
month-to-month3875
37.9%
year3168
31.0%
two1695
16.6%
one1473
 
14.4%

Most occurring characters

ValueCountFrequency (%)
o13320
16.7%
t11625
14.6%
n9223
11.6%
h7750
9.7%
-7750
9.7%
e4641
 
5.8%
M3875
 
4.9%
m3875
 
4.9%
3168
 
4.0%
y3168
 
4.0%
Other values (5)11199
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter61633
77.4%
Dash Punctuation7750
 
9.7%
Uppercase Letter7043
 
8.8%
Space Separator3168
 
4.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o13320
21.6%
t11625
18.9%
n9223
15.0%
h7750
12.6%
e4641
 
7.5%
m3875
 
6.3%
y3168
 
5.1%
a3168
 
5.1%
r3168
 
5.1%
w1695
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
M3875
55.0%
T1695
24.1%
O1473
 
20.9%
Dash Punctuation
ValueCountFrequency (%)
-7750
100.0%
Space Separator
ValueCountFrequency (%)
3168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin68676
86.3%
Common10918
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o13320
19.4%
t11625
16.9%
n9223
13.4%
h7750
11.3%
e4641
 
6.8%
M3875
 
5.6%
m3875
 
5.6%
y3168
 
4.6%
a3168
 
4.6%
r3168
 
4.6%
Other values (3)4863
 
7.1%
Common
ValueCountFrequency (%)
-7750
71.0%
3168
29.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII79594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o13320
16.7%
t11625
14.6%
n9223
11.6%
h7750
9.7%
-7750
9.7%
e4641
 
5.8%
M3875
 
4.9%
m3875
 
4.9%
3168
 
4.0%
y3168
 
4.0%
Other values (5)11199
14.1%

Paperless Billing
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
True
4171 
False
2872 
ValueCountFrequency (%)
True4171
59.2%
False2872
40.8%
2022-09-11T17:53:39.991288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Payment Method
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
Electronic check
2365 
Mailed check
1612 
Bank transfer (automatic)
1544 
Credit card (automatic)
1522 

Length

Max length25
Median length23
Mean length18.57021156
Min length12

Characters and Unicode

Total characters130790
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMailed check
2nd rowElectronic check
3rd rowElectronic check
4th rowElectronic check
5th rowBank transfer (automatic)

Common Values

ValueCountFrequency (%)
Electronic check2365
33.6%
Mailed check1612
22.9%
Bank transfer (automatic)1544
21.9%
Credit card (automatic)1522
21.6%

Length

2022-09-11T17:53:40.024079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:40.063823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
check3977
23.2%
automatic3066
17.9%
electronic2365
13.8%
mailed1612
9.4%
bank1544
 
9.0%
transfer1544
 
9.0%
credit1522
 
8.9%
card1522
 
8.9%

Most occurring characters

ValueCountFrequency (%)
c17272
13.2%
a12354
 
9.4%
t11563
 
8.8%
e11020
 
8.4%
10109
 
7.7%
i8565
 
6.5%
r8497
 
6.5%
k5521
 
4.2%
n5453
 
4.2%
o5431
 
4.2%
Other values (13)35005
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter107506
82.2%
Space Separator10109
 
7.7%
Uppercase Letter7043
 
5.4%
Open Punctuation3066
 
2.3%
Close Punctuation3066
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c17272
16.1%
a12354
11.5%
t11563
10.8%
e11020
10.3%
i8565
8.0%
r8497
7.9%
k5521
 
5.1%
n5453
 
5.1%
o5431
 
5.1%
d4656
 
4.3%
Other values (6)17174
16.0%
Uppercase Letter
ValueCountFrequency (%)
E2365
33.6%
M1612
22.9%
B1544
21.9%
C1522
21.6%
Space Separator
ValueCountFrequency (%)
10109
100.0%
Open Punctuation
ValueCountFrequency (%)
(3066
100.0%
Close Punctuation
ValueCountFrequency (%)
)3066
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin114549
87.6%
Common16241
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
c17272
15.1%
a12354
10.8%
t11563
10.1%
e11020
9.6%
i8565
 
7.5%
r8497
 
7.4%
k5521
 
4.8%
n5453
 
4.8%
o5431
 
4.7%
d4656
 
4.1%
Other values (10)24217
21.1%
Common
ValueCountFrequency (%)
10109
62.2%
(3066
 
18.9%
)3066
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII130790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c17272
13.2%
a12354
 
9.4%
t11563
 
8.8%
e11020
 
8.4%
10109
 
7.7%
i8565
 
6.5%
r8497
 
6.5%
k5521
 
4.2%
n5453
 
4.2%
o5431
 
4.2%
Other values (13)35005
26.8%

Monthly Charges
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1585
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.76169246
Minimum18.25
Maximum118.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-09-11T17:53:40.104078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18.25
5-th percentile19.65
Q135.5
median70.35
Q389.85
95-th percentile107.4
Maximum118.75
Range100.5
Interquartile range (IQR)54.35

Descriptive statistics

Standard deviation30.0900471
Coefficient of variation (CV)0.4646272504
Kurtosis-1.257259695
Mean64.76169246
Median Absolute Deviation (MAD)24.05
Skewness-0.2205244339
Sum456116.6
Variance905.4109343
MonotonicityNot monotonic
2022-09-11T17:53:40.149606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0561
 
0.9%
19.8545
 
0.6%
19.9544
 
0.6%
19.944
 
0.6%
19.6543
 
0.6%
2043
 
0.6%
19.743
 
0.6%
20.1540
 
0.6%
19.5540
 
0.6%
19.7539
 
0.6%
Other values (1575)6601
93.7%
ValueCountFrequency (%)
18.251
 
< 0.1%
18.41
 
< 0.1%
18.551
 
< 0.1%
18.72
 
< 0.1%
18.751
 
< 0.1%
18.87
0.1%
18.855
0.1%
18.92
 
< 0.1%
18.956
0.1%
197
0.1%
ValueCountFrequency (%)
118.751
< 0.1%
118.651
< 0.1%
118.62
< 0.1%
118.351
< 0.1%
118.21
< 0.1%
117.81
< 0.1%
117.61
< 0.1%
117.51
< 0.1%
117.451
< 0.1%
117.351
< 0.1%

Total Charges
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6530
Distinct (%)92.9%
Missing11
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2283.300441
Minimum18.8
Maximum8684.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-09-11T17:53:40.193311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18.8
5-th percentile49.605
Q1401.45
median1397.475
Q33794.7375
95-th percentile6923.59
Maximum8684.8
Range8666
Interquartile range (IQR)3393.2875

Descriptive statistics

Standard deviation2266.771362
Coefficient of variation (CV)0.992760883
Kurtosis-0.2317987609
Mean2283.300441
Median Absolute Deviation (MAD)1222.8
Skewness0.9616424997
Sum16056168.7
Variance5138252.407
MonotonicityNot monotonic
2022-09-11T17:53:40.240542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.211
 
0.2%
19.759
 
0.1%
19.658
 
0.1%
20.058
 
0.1%
19.98
 
0.1%
19.557
 
0.1%
45.37
 
0.1%
20.156
 
0.1%
20.256
 
0.1%
19.456
 
0.1%
Other values (6520)6956
98.8%
(Missing)11
 
0.2%
ValueCountFrequency (%)
18.81
 
< 0.1%
18.852
< 0.1%
18.91
 
< 0.1%
191
 
< 0.1%
19.051
 
< 0.1%
19.13
< 0.1%
19.151
 
< 0.1%
19.24
0.1%
19.253
< 0.1%
19.34
0.1%
ValueCountFrequency (%)
8684.81
< 0.1%
8672.451
< 0.1%
8670.11
< 0.1%
8594.41
< 0.1%
8564.751
< 0.1%
8547.151
< 0.1%
8543.251
< 0.1%
8529.51
< 0.1%
8496.71
< 0.1%
8477.71
< 0.1%

Churn Label
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 KiB
False
5174 
True
1869 
ValueCountFrequency (%)
False5174
73.5%
True1869
 
26.5%
2022-09-11T17:53:40.280694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Churn Value
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
0
5174 
1
1869 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7043
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
05174
73.5%
11869
 
26.5%

Length

2022-09-11T17:53:40.313183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-11T17:53:40.349126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
05174
73.5%
11869
 
26.5%

Most occurring characters

ValueCountFrequency (%)
05174
73.5%
11869
 
26.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7043
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05174
73.5%
11869
 
26.5%

Most occurring scripts

ValueCountFrequency (%)
Common7043
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05174
73.5%
11869
 
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII7043
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05174
73.5%
11869
 
26.5%

Churn Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct85
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.69941786
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-09-11T17:53:40.386153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile24
Q140
median61
Q375
95-th percentile94
Maximum100
Range95
Interquartile range (IQR)35

Descriptive statistics

Standard deviation21.52513068
Coefficient of variation (CV)0.3667009225
Kurtosis-1.005679127
Mean58.69941786
Median Absolute Deviation (MAD)17
Skewness-0.08983998912
Sum413420
Variance463.3312507
MonotonicityNot monotonic
2022-09-11T17:53:40.431506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80151
 
2.1%
71148
 
2.1%
77145
 
2.1%
67143
 
2.0%
76141
 
2.0%
68141
 
2.0%
70140
 
2.0%
69140
 
2.0%
78138
 
2.0%
72137
 
1.9%
Other values (75)5619
79.8%
ValueCountFrequency (%)
51
 
< 0.1%
72
 
< 0.1%
82
 
< 0.1%
93
 
< 0.1%
2083
1.2%
2184
1.2%
2282
1.2%
2378
1.1%
2486
1.2%
2585
1.2%
ValueCountFrequency (%)
10050
0.7%
9954
0.8%
9850
0.7%
9764
0.9%
9652
0.7%
9543
0.6%
9446
0.7%
9347
0.7%
9248
0.7%
9145
0.6%

CLTV
Real number (ℝ≥0)

Distinct3438
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4400.295755
Minimum2003
Maximum6500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.1 KiB
2022-09-11T17:53:40.610410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2296
Q13469
median4527
Q35380.5
95-th percentile6087
Maximum6500
Range4497
Interquartile range (IQR)1911.5

Descriptive statistics

Standard deviation1183.057152
Coefficient of variation (CV)0.2688585536
Kurtosis-0.934032483
Mean4400.295755
Median Absolute Deviation (MAD)922
Skewness-0.3116021004
Sum30991283
Variance1399624.225
MonotonicityNot monotonic
2022-09-11T17:53:40.655834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55468
 
0.1%
47417
 
0.1%
55277
 
0.1%
50927
 
0.1%
47457
 
0.1%
54617
 
0.1%
51377
 
0.1%
41157
 
0.1%
22697
 
0.1%
43697
 
0.1%
Other values (3428)6972
99.0%
ValueCountFrequency (%)
20033
< 0.1%
20043
< 0.1%
20061
 
< 0.1%
20074
0.1%
20081
 
< 0.1%
20092
< 0.1%
20103
< 0.1%
20112
< 0.1%
20132
< 0.1%
20141
 
< 0.1%
ValueCountFrequency (%)
65001
 
< 0.1%
64992
< 0.1%
64951
 
< 0.1%
64942
< 0.1%
64923
< 0.1%
64911
 
< 0.1%
64901
 
< 0.1%
64891
 
< 0.1%
64881
 
< 0.1%
64872
< 0.1%

Churn Reason
Categorical

HIGH CORRELATION
MISSING

Distinct20
Distinct (%)1.1%
Missing5174
Missing (%)73.5%
Memory size55.1 KiB
Attitude of support person
192 
Competitor offered higher download speeds
189 
Competitor offered more data
162 
Don't know
154 
Competitor made better offer
140 
Other values (15)
1032 

Length

Max length41
Median length31
Mean length25.19422151
Min length5

Characters and Unicode

Total characters47088
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompetitor made better offer
2nd rowMoved
3rd rowMoved
4th rowMoved
5th rowCompetitor had better devices

Common Values

ValueCountFrequency (%)
Attitude of support person192
 
2.7%
Competitor offered higher download speeds189
 
2.7%
Competitor offered more data162
 
2.3%
Don't know154
 
2.2%
Competitor made better offer140
 
2.0%
Attitude of service provider135
 
1.9%
Competitor had better devices130
 
1.8%
Network reliability103
 
1.5%
Product dissatisfaction102
 
1.4%
Price too high98
 
1.4%
Other values (10)464
 
6.6%
(Missing)5174
73.5%

Length

2022-09-11T17:53:40.697906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitor621
 
9.5%
of542
 
8.3%
offered351
 
5.3%
attitude327
 
5.0%
better270
 
4.1%
support231
 
3.5%
service224
 
3.4%
data219
 
3.3%
person192
 
2.9%
dissatisfaction191
 
2.9%
Other values (37)3396
51.7%

Most occurring characters

ValueCountFrequency (%)
e5658
12.0%
o4751
 
10.1%
4695
 
10.0%
t4427
 
9.4%
r3512
 
7.5%
i3114
 
6.6%
d2659
 
5.6%
s2225
 
4.7%
a1942
 
4.1%
f1891
 
4.0%
Other values (27)12214
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter40150
85.3%
Space Separator4695
 
10.0%
Uppercase Letter1957
 
4.2%
Other Punctuation198
 
0.4%
Dash Punctuation88
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5658
14.1%
o4751
11.8%
t4427
11.0%
r3512
8.7%
i3114
 
7.8%
d2659
 
6.6%
s2225
 
5.5%
a1942
 
4.8%
f1891
 
4.7%
p1746
 
4.3%
Other values (13)8225
20.5%
Uppercase Letter
ValueCountFrequency (%)
C621
31.7%
A327
16.7%
P239
 
12.2%
L220
 
11.2%
D160
 
8.2%
N103
 
5.3%
S89
 
4.5%
W88
 
4.5%
E57
 
2.9%
M53
 
2.7%
Other Punctuation
ValueCountFrequency (%)
'154
77.8%
/44
 
22.2%
Space Separator
ValueCountFrequency (%)
4695
100.0%
Dash Punctuation
ValueCountFrequency (%)
-88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin42107
89.4%
Common4981
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5658
13.4%
o4751
11.3%
t4427
10.5%
r3512
 
8.3%
i3114
 
7.4%
d2659
 
6.3%
s2225
 
5.3%
a1942
 
4.6%
f1891
 
4.5%
p1746
 
4.1%
Other values (23)10182
24.2%
Common
ValueCountFrequency (%)
4695
94.3%
'154
 
3.1%
-88
 
1.8%
/44
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII47088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e5658
12.0%
o4751
 
10.1%
4695
 
10.0%
t4427
 
9.4%
r3512
 
7.5%
i3114
 
6.6%
d2659
 
5.6%
s2225
 
4.7%
a1942
 
4.1%
f1891
 
4.0%
Other values (27)12214
25.9%

Interactions

2022-09-11T17:53:37.586400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:34.954012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.832947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.104892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.383334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.670371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.032187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.310775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.621872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:34.983838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.867686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.140738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.420307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.706205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.070017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.347276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.655682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.346271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.900502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.174455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.455109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.740187image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.104471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.380930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.690066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.553509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.934871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.209458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.491174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.775330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.140008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.415271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.729120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.692364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.970427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.245673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.528567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.812110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.175873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.451185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.763515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.728046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.004565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.281251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.564970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.849317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.210243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.486219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.796824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.763269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.037971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.315395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.600031image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.883154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.243600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.519549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.829938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:35.798383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.071407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.349603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.635011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:36.917019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.277248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-11T17:53:37.553431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-09-11T17:53:40.734606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-11T17:53:40.825234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-11T17:53:40.912962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-11T17:53:41.007960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-11T17:53:41.120466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-11T17:53:37.914912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-11T17:53:38.096186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-11T17:53:38.177244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-11T17:53:38.229952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CustomerIDCountCountryStateCityZip CodeLat LongLatitudeLongitudeGenderSenior CitizenPartnerDependentsTenure MonthsPhone ServiceMultiple LinesInternet ServiceOnline SecurityOnline BackupDevice ProtectionTech SupportStreaming TVStreaming MoviesContractPaperless BillingPayment MethodMonthly ChargesTotal ChargesChurn LabelChurn ValueChurn ScoreCLTVChurn Reason
03668-QPYBK1United StatesCaliforniaLos Angeles9000333.964131, -118.27278333.964131-118.272783MaleNoNoNo2YesNoDSLYesYesNoNoNoNoMonth-to-monthYesMailed check53.85108.15Yes1863239Competitor made better offer
19237-HQITU1United StatesCaliforniaLos Angeles9000534.059281, -118.3074234.059281-118.307420FemaleNoNoYes2YesNoFiber opticNoNoNoNoNoNoMonth-to-monthYesElectronic check70.70151.65Yes1672701Moved
29305-CDSKC1United StatesCaliforniaLos Angeles9000634.048013, -118.29395334.048013-118.293953FemaleNoNoYes8YesYesFiber opticNoNoYesNoYesYesMonth-to-monthYesElectronic check99.65820.50Yes1865372Moved
37892-POOKP1United StatesCaliforniaLos Angeles9001034.062125, -118.31570934.062125-118.315709FemaleNoYesYes28YesYesFiber opticNoNoYesYesYesYesMonth-to-monthYesElectronic check104.803046.05Yes1845003Moved
40280-XJGEX1United StatesCaliforniaLos Angeles9001534.039224, -118.26629334.039224-118.266293MaleNoNoYes49YesYesFiber opticNoYesYesNoYesYesMonth-to-monthYesBank transfer (automatic)103.705036.30Yes1895340Competitor had better devices
54190-MFLUW1United StatesCaliforniaLos Angeles9002034.066367, -118.30986834.066367-118.309868FemaleNoYesNo10YesNoDSLNoNoYesYesNoNoMonth-to-monthNoCredit card (automatic)55.20528.35Yes1785925Competitor offered higher download speeds
68779-QRDMV1United StatesCaliforniaLos Angeles9002234.02381, -118.15658234.023810-118.156582MaleYesNoNo1NoNo phone serviceDSLNoNoYesNoNoYesMonth-to-monthYesElectronic check39.6539.65Yes11005433Competitor offered more data
71066-JKSGK1United StatesCaliforniaLos Angeles9002434.066303, -118.43547934.066303-118.435479MaleNoNoNo1YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceMonth-to-monthNoMailed check20.1520.15Yes1924832Competitor made better offer
86467-CHFZW1United StatesCaliforniaLos Angeles9002834.099869, -118.32684334.099869-118.326843MaleNoYesYes47YesYesFiber opticNoYesNoNoYesYesMonth-to-monthYesElectronic check99.354749.15Yes1775789Competitor had better devices
98665-UTDHZ1United StatesCaliforniaLos Angeles9002934.089953, -118.29482434.089953-118.294824MaleNoYesNo1NoNo phone serviceDSLNoYesNoNoNoNoMonth-to-monthNoElectronic check30.2030.20Yes1972915Competitor had better devices

Last rows

CustomerIDCountCountryStateCityZip CodeLat LongLatitudeLongitudeGenderSenior CitizenPartnerDependentsTenure MonthsPhone ServiceMultiple LinesInternet ServiceOnline SecurityOnline BackupDevice ProtectionTech SupportStreaming TVStreaming MoviesContractPaperless BillingPayment MethodMonthly ChargesTotal ChargesChurn LabelChurn ValueChurn ScoreCLTVChurn Reason
70330871-OPBXW1United StatesCaliforniaTwentynine Palms9227734.17211, -115.76977334.172110-115.769773FemaleNoNoNo2YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceMonth-to-monthYesMailed check20.0539.25No0805191NaN
70343605-JISKB1United StatesCaliforniaTwentynine Palms9227834.457829, -116.13958934.457829-116.139589MaleYesYesNo55YesYesDSLYesYesNoNoNoNoOne yearNoCredit card (automatic)60.003316.10No0714212NaN
70359767-FFLEM1United StatesCaliforniaWestmorland9228133.03679, -115.6050333.036790-115.605030MaleNoNoNo38YesNoFiber opticNoNoNoNoNoNoMonth-to-monthYesCredit card (automatic)69.502625.25No0354591NaN
70368456-QDAVC1United StatesCaliforniaWinterhaven9228332.852947, -114.85078432.852947-114.850784MaleNoNoNo19YesNoFiber opticNoNoNoNoYesNoMonth-to-monthYesBank transfer (automatic)78.701495.10No0202464NaN
70377750-EYXWZ1United StatesCaliforniaYucca Valley9228434.159534, -116.42598434.159534-116.425984FemaleNoNoNo12NoNo phone serviceDSLNoYesYesYesYesYesOne yearNoElectronic check60.65743.30No0243740NaN
70382569-WGERO1United StatesCaliforniaLanders9228534.341737, -116.53941634.341737-116.539416FemaleNoNoNo72YesNoNoNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceNo internet serviceTwo yearYesBank transfer (automatic)21.151419.40No0455306NaN
70396840-RESVB1United StatesCaliforniaAdelanto9230134.667815, -117.53618334.667815-117.536183MaleNoYesYes24YesYesDSLYesNoYesYesYesYesOne yearYesMailed check84.801990.50No0592140NaN
70402234-XADUH1United StatesCaliforniaAmboy9230434.559882, -115.63716434.559882-115.637164FemaleNoYesYes72YesYesFiber opticNoYesYesNoYesYesOne yearYesCredit card (automatic)103.207362.90No0715560NaN
70414801-JZAZL1United StatesCaliforniaAngelus Oaks9230534.1678, -116.8643334.167800-116.864330FemaleNoYesYes11NoNo phone serviceDSLYesNoNoNoNoNoMonth-to-monthYesElectronic check29.60346.45No0592793NaN
70423186-AJIEK1United StatesCaliforniaApple Valley9230834.424926, -117.18450334.424926-117.184503MaleNoNoNo66YesNoFiber opticYesNoYesYesYesYesTwo yearYesBank transfer (automatic)105.656844.50No0385097NaN